Earth Observation for Crop Modelling

Transcription

1 Earth Observation for Crop Modelling International trends & developments How to promote earth observation applications? How to get funding? Capacity building

2 0. Introduction Mark Noort, consultant, project manager HCP international: consulting, marketing of earth observation Coordinator GEONetCab: project for promotion & capacity building of earth observation applications 2

3 Earth observation applications On the verge of reaching new user communities These new user communities need to be involved Weakest link / last mile aspects are important Marketing needed: promotion & capacity building 3

4 Life cycle of products & services Initialization System analysis & design Rapid prototyping System development Implementation Post-implementation 4

5 Assessment of business & funding opportunities Categories of environmental management products & services Life cycle phase of product or service Regional context, level of technological & economic development Optimum marketing mix 5

6 1. International trends & developments in crop modelling 6

7 Climate change Climate change becomes more and more important, local adaptation focuses on disaster management and food security References: 7

8 Western Australia: WA Agriculture Climate Science Example of how to be prepared : Aims at better prediction of: yields (yield prophet), changing land use on unproductive soils, preparedness for drought (off-farm employment & new farming systems). This is done by: soil characterization, biomass estimation & crop yield from space, cropping decisions based on Yield Prophet, economic resilience index, carbon sequestration on native vegetation (poor soils). Real-time & local weather information + seasonal forecasting + historical analysis: farmers like the short-term forecasts (also of extreme events), but critical of seasonal forecasts. Early part of season is critical for decision on sowing, fertilizer rate, weed & pest control. Climate change perception: what is influencing attitudes (credibility of science versus local knowledge, how does information translate into action)? 8

9 Climate Change and Food Security: A framework document Description of processes and possible adaptive measures 9

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12 Markets, Climate Change, and Food Security in West Africa (Brown, Hintermann, Higgins) Effects of climate change difficult to predict, but possible reduction of agricultural production up to 50% by 2020: increase resilience! Increase agricultural production: affordable technology, financial assistance, high-yielding agricultural seeds and fertilizer, small-scale storage facilities, mobile phone fund transfers, microloans + crop insurance, community-based natural resource programs. 12

13 Empowerment of local communities Community mapping initiatives: Google (H2O) and World Bank Mapping & visualization in general: importance of knowing what is where Example: World Bank mapping for maps.worldbank.org & aiddata.org (tracking development finance) Crop modelling: better information provision to improve quantity and quality of production. References: 13

14 Bridging the rural digital divide e-agriculture A Definition and Profile of its Application (FAO, 2005) description of global trends and FAO policy GIEWS workstation (Global Information and Early Warning System) FAO description of services offered to 20 countries in Africa, CIS, SE Asia and Central America Space Technology Enabled Village Resource Centre (VRC) description of societal benefit support to villages: tele-education, tele-healthcare, land and water resources management, tele-fishery, e-governance services and weather services by the Indian Space Research Organization (ISRO) 14

15 Food Prices and Markets FAO: handle protracted crises long-term assistance, social protection mechanisms (school meals, vouchers), stimulating markets (food-aid through local markets, cashbased schemes) The State of Food Security in the World (2010) World Bank / IMF : better manage risks and reduce future vulnerabilities (insurance, the use of weather derivatives, accurate weather forecasting, warehouse receipts and commodity exchanges, and public stock management) Responding to Global Food Price Volatility and its Impact on Food Security (2011) IFC: Global Agriculture Price Risk Management Facility WFP: earth observation as support tool for food-aid logistics G20: improve weather forecasting and monitoring, Agricultural Market Information System (AMIS), Global Agricultural Monitoring (GLAM), agriculture and food security risk management toolbox Action Plan on Food Price Volatility and Agriculture, Ministerial Declaration (2011) 15

16 Studies on food prices and markets: NASA / University of Maryland: The effect of vegetation productivity on millet prices in the informal markets of Mali, Burkina Faso and Niger (Brown, Pinzon, Prince; 2006) describes the relation between NDVI and millet prices in West- Africa Several Michican State University International Development Working Papers example: Spatial Patterns of Food Staple Production and Marketing in South East Africa: Implications for Trade Policy and Emergency Response (Haggblade, Longabaugh, Tschirley; 2009) analyzes total market and production system, not only crop modeling 16

17 Historical Analysis Time dimension very important as input for decision-making: Crop Modelling Climate change monitoring and adaptation Change detection (depletion of natural resources) Analyzing the effects of different policies, practices, customs, across administrative borders Example: SPOT VGT difference products offered by VITO (every 10 days): Scaled Difference Vegetation Index, Vegetation Condition Index, absolute difference, etc. 17

18 Improved Prediction Examples of global systems for prediction of food shortages and timely planning of aid operations: FEWSNET GIEWS (FAO) GMFS FOOD-SEC (MARS) 18

19 Improved Prediction Other examples: GLAM (global agricultural monitoring project) timely data delivery, continuity of EO missions, enhanced value added products, yield models, crop area estimates and seasonal weather forecasts, interoperability and better integration of datasets Harvest Choice data harmonization (assembling heterogeneous datasets) + data distribution (new ways of collecting and distributing spatial data) CanaSat project sugarcane area mapping and harvest monitoring (environmental protection) in Brazil 19

20 Insurance Very much in focus as part of agricultural risk management strategy (World Bank reports: Managing Agricultural Production Risk & Agricultural Insurance) Example of use of earth observation: ADASCIS, Belgium The use of remote sensing and agrometeorological modelling for crop damage & risk assessment in support of the Belgian Calamity Fund Business model developed countries: more about income transfers then about risk management indemnities paid about equal to total premiums collected, but administrative and operation costs not taken into account. 20

21 Insurance Index-based, weather-related agricultural insurance Based on: rainfall or NDVI Spain, Canada, Mongolia, Kenya, Mexico (all pasture / rangeland -> for crops apparent low correlation NDVI and yields) or evapotranspiration Food Early Solutions for Africa (FESA) in experimental stage Should be: observable and easily measured, objective, transparent, independently verifyable, reportable in a timely manner & stable and sustainable over time Products best suited for: homogeneous areas, systemic risk at the aggregated level (reinsurance) Historical analysis (weather, crops) is important 21

22 Insurance References and case studies: The Potential for Scale and Sustainability in Weather Index Insurance for Agriculture and Rural Livelihoods (IFAD, WFP) Insurance as an Adaptation Measure to Climate Variability in Agriculture (CEIGRAM, Spain) Comprehensive Risk Cover through Remote Sensing Techniques in Agriculture Insurance for Developing Countries: a pilot project (India) Providing index-based agricultural insurance to smallholders: recent progress and future promise (Univ. of California, Berkeley) Agricultural Insurance Schemes (JRC, European Union) 22

23 Insurance References and case studies (continued): Index Based Livestock Insurance for Northern Kenya s Arid and Semi-Arid Lands: the Marsabit Pilot (ILRI & partners) + Designing Index Based Livestock Insurance for Managing Asset Risk in Northern Kenya Index Based Agricultural Insurance in Developing Countries: Feasibility, Scalability and Sustainability (Montana State University) Science-based insurance (Brown, Osgood, Carriquiry) Contributions of Agricultural Systems Modeling to Weather Index Insurance (Baethgen et al) State of Knowledge Report Data Requirements for the Design of Weather Index Insurance: Innovation in Catastrophic Weather Insurance to Improve the Livelyhood of Rural Households (GlobalAgRisk) 23

24 Insurance Farmers perception: Difficulty in understanding index-based concepts, what satellite images can do and concerns about visualizing satellite-based indices + calculating mean vegetation when sowing dates are different Local companies experience: Calculating NDVI is expensive (one-off for GIS company) 24

25 Other Risk Management Comprehensive Food Security and Vulnerability Analysis (CFSVA) Baseline mapping Agricultural monitoring: background information, estimation of area cultivated, yield estimation (all include historical analysis) Disaster assessment Operational planning Other example: Locust Habitat Monitoring for GIEWS (see also disaster management toolkit) 25

26 2. Steps to promote earth observation for crop modelling 26

27 State-of-the-art Earth observation is new technology. Learn technical skills, but when back in professional practice, it has to be put to good use. That involves selling it. How to do that? To whom? Could be your own boss, local authorities, communities, etc. 27

28 References Western Australia: WA Agriculture Climate Science FAO: Climate Change and Food Security: a framework document Added value earth observation: Crop modelling is one of the contributors to food security (as part of value chain management), Facilitates implementation of insurance schemes, Helps manage water more efficiently, Supports sustainable land management, Helps maintaining biodiversity, Improves livestock management, Improves fishery management. 28

29 References (continued) CanaSat (Brazil) see above e-leaf (eleaf.com) offers integrated agricultural services to farmers in the Netherlands and Europe pilot phase GEO Task US-09-01a: Critical Earth Observations Priorities Agriculture Societal Benefit Area global overview of available observations and what is needed for more examples: have a look at 29

30 GLobal Agricultural Monitoring community GLAM: what is needed? 30

31 Marketing of earth observation Marketing of earth observation is difficult. New technology, few big companies, lots of small ones. Lots of reports describing the bottlenecks, like reliability, data access, data continuity, etc. Means that relatively a lot of effort is needed to promote EO. 31

32 Points to keep in mind: Look for opportunities, where can you have most success in a short time: quick-wins. Target the right audience to start with: who would be interested and listen to you? For crop modelling: see next slides. Identify the problem that they are trying to solve: is it the same as yours? Learn to speak the same language. Example: when EO specialists talk about food security, experts in other fields consider EO activities as crop modelling. Translate to something the client (or your partner) understands. Look for examples from elsewhere (success stories): solutions that work and are affordable. 32

33 What users need to serve clients: Accurate and reliable crop production information on specific reporting levels (national, district) Spatial distribution of cultivated area Crop growth models Timely and unbiased production information on main crops (Timely) availability of satellite data Good quality meteorological data Capacity building to enable correct use and integration of products and services 33

34 Figure 2: Advanced multi scale crop information services (GMFS services, CA = Cultivated Area; CEP = Crop Emergence Period; EoC = Extent of Cultivation; DMP = Dry Matter Productivity; VPI= Vegetation Productivity Indicator). 34

35 Maturity of EO Services Early warning: fully operational Cultivated area: further development needed (timely delivery of data, technical complexity) Extent of cultivation: accuracy needs to be improved for good local results (district, farm) Crop yield forecasting: fully operational Index-based insurance: better results for rangeland than for crops, especially for smallholders (NDVI-based, cloud cover a problem) Precision agriculture: still in pilot phase (cloud cover in critical months of the growing season is a problem) 35

36 Costs of EO Services for crop modelling / food security support Service Definition of Unit Service Cost (k ) Early Warning Annual coverage of Africa with suite 230 Service of early warning indicators, at 10-day update frequency Agricultural Validated map of cultivated area 175 Mapping (10 20 m resolution) per km 2 Crop Yield Yield forecast for 2-3 main crops, 85 Assessment for km 2 Support to Support to one CFSVA mission 35 CFSVA very global estimate, includes development for specific user needs and training 36

37 Be patient: introduction of new technology and / or applications takes time 37

38 3. How to get funding for your activities 38

39 Approach Share information on your subject (a thing you are doing) and think that is interesting for your contact, then look for the link. Could this solve a problem for your partner? Are adjustments necessary? Need other parties be involved? Take it from there. LEADS, LEADS, LEADS 39

40 How? Establish your network. Look for opportunities. Write a good proposal. Promise much, but not too much. 40

41 Proposal outline (more detailed version in separate document, see also ) 1. Introduction / relevance 2. Objective(s) 3. Activities 4. Output 5. Management & evaluation 6. Risk assessment 7. Time schedule 8. Budget Annexes 41

42 Other references Civicus: writing a funding proposal Michigan State University: guide for writing a funding proposal ESRI: writing a competitive GRANT application REC: project proposal writing 42

43 Again: SHARED PROBLEM SHARED LANGUAGE SHARED SOLUTION If all else fails, try to link with a more popular (and easy to understand) topic. 43

44 4. Capacity Building 44

45 General Marketing is promotion + capacity building. Especially for the introduction of new technologies capacity building is important at all levels. Capacity building is the instrument to increase self-sufficiency and make solutions work. 45

46 Think of: Different instruments for different levels: workshops for decision makers and awareness raising, detailed technical training for professionals. Provide follow-up. Getting funding for good capacity building is difficult: everybody agrees that it is important, but nobody has time. Training is usually part of funding of big projects that are managed by big companies or ministries, as a consequence capacity building is forgotten (in the end). Aim at small budgets that are available without having to tender. 46

47 Examples & references AGRICAB project (follow-up of DevCoCast and GMFS) optical remote sensing, radar remote sensing, agro-meteorological modelling, food security information systems, product validation GEONetCab capacity building web compilation of tutorials, references, open-source software, etc. GEO Portal: Focal points: cheaper processing of NDVI calculations, ground truthing, historical analysis (making use of free and open data): integration with other services 47

48 More references A Rough Google Earth Guide MEASURE Evaluation Global Positioning System Toolkit (USAID) Handbook of Research on Developments and Trends in Wireless Sensor Networks: From Principle to Practice 48

49 Further details: Contact: Mark Noort 49